Airbnb - relation between units’ rent & Airbnb revenue on SA2 level

Models - nlme

1 Selection

Minimum 25 rental properties. Minimum 10 Airbnbs. Per month.

summary(APM_sa2_cor$mean_airbnb)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.19   24.70   42.44   98.78   94.26 3050.81

summary(APM_sa2_cor$mean_apm)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   29.82  125.44  240.56  358.67  443.70 4483.07

summary(APM_sa2_cor$min_airbnb)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.00   15.00   25.00   66.14   62.00 1782.00

summary(APM_sa2_cor$min_apm)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    25.0   108.0   213.0   321.3   391.0  4235.0

2 Data

Units only

2.1 Number of areas

length(unique(data$SA2_MAIN16))

## [1] 355

data %>% 
  select(SA2_MAIN16, STE_NAME16) %>% 
  distinct() %>% 
  group_by(STE_NAME16) %>% 
  summarize(areas = n())

## # A tibble: 8 x 2
##   STE_NAME16                   areas
##   <chr>                        <int>
## 1 Australian Capital Territory     8
## 2 New South Wales                124
## 3 Northern Territory               2
## 4 Queensland                      85
## 5 South Australia                 16
## 6 Tasmania                        11
## 7 Victoria                        78
## 8 Western Australia               31

2.2 Temporal trends

ggplot(data, aes(x = date, y = rent_50p_int)) +
  geom_line(aes(group = SA2_MAIN16), alpha = .5) +
  # geom_smooth(method = "lm", se = FALSE) + 
  geom_smooth(aes(weight = record_count)) +
  theme_minimal() + xlab("") + ylab("Rent") +
  facet_wrap(~STE_NAME16)

ggplot(data, aes(x = date, y = adr_50p_int)) +
  geom_line(aes(group = SA2_MAIN16), alpha = .5) +
  # geom_smooth(method = "lm", se = FALSE) + 
  geom_smooth(aes(weight = n_listings_int)) +
  theme_minimal() + xlab("") + ylab("AWR") +
  facet_wrap(~STE_NAME16)

2.3 Rent vs AWR

2.3.1 All 27 months

data %>% 
  filter(STE_NAME16 != "Northern Territory") %>%
  ggplot(aes(x = rent_50p_int, y = adr_50p_int)) +
  geom_point(aes(size = n_listings_int)) +
  # geom_smooth(aes(weight = n_listings_int), method = "lm", se = FALSE)
  geom_smooth(aes(weight = n_listings_int)) +
  theme_minimal() + xlab("Rent") + ylab("AWR") +
  facet_wrap(~STE_NAME16)

2.3.2 All areas by month

data %>% 
  filter(STE_NAME16 != "Northern Territory") %>%
  filter(STE_NAME16 != "Australian Capital Territory") %>%
  ggplot(aes(x = rent_50p_int, y = adr_50p_int)) +
  # geom_point(aes(size = n_listings_int, col = STE_NAME16)) +
  # geom_smooth(aes(weight = n_listings_int), method = "lm", se = FALSE) +
  geom_smooth(aes(weight = n_listings_int, col = STE_NAME16)) +
  facet_wrap(~time) +
  theme_minimal() + xlab("Rent") + ylab("AWR") +
  theme(strip.text.x = element_blank()) 

2.3.3 Last month

data %>% 
  filter(STE_NAME16 != "Northern Territory") %>%
  filter(time == 27) %>%
  ggplot(aes(x = rent_50p_int, y = adr_50p_int)) +
  geom_point(aes(size = n_listings_int)) +
  # geom_smooth(aes(weight = n_listings_int), method = "lm", se = FALSE)
  geom_smooth(aes(weight = n_listings_int)) +
  theme_minimal() + xlab("Rent") + ylab("AWR") +
  facet_wrap(~STE_NAME16)

2.4 Rent over time

# rent linear
data %>% 
  # filter(STE_NAME16 != "Northern Territory") %>%
  ggplot(aes(x = time, y = rent_50p_int/100)) +
  # annotate("text", x = 0, y = 200, label = unique(data$SA2_NAME16)) +
  # geom_point(aes(size = record_count), alpha = 0.3) +
  geom_smooth(aes(group = interaction(SA2_NAME16, STE_NAME16),
                  weight = record_count_int,
                  col = STE_NAME16), 
              method = "lm", se = FALSE) +
  facet_wrap(STE_NAME16~SA2_NAME16) +
  theme_minimal() + xlab("") + ylab(("")) +
  theme(strip.text.x = element_blank()) 

# rent smooth
data %>% 
  # filter(STE_NAME16 != "Northern Territory") %>%
  ggplot(aes(x = time, y = rent_50p_int/100)) +
  # annotate("text", x = 0, y = 200, label = unique(data$SA2_NAME16)) +
  # geom_point(aes(size = record_count), alpha = 0.3) +
  geom_smooth(aes(group = interaction(SA2_NAME16, STE_NAME16),
                  weight = n_listings_int,
                  col = STE_NAME16), se = FALSE) +
  facet_wrap(STE_NAME16~SA2_NAME16) +
  theme_minimal() + xlab("") + ylab(("")) +
  theme(strip.text.x = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(), 
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())

2.5 AWR over time

## adr, linear
ggplot(data, aes(x = time, y = adr_50p_int/1000, group = SA2_NAME16)) +
  # annotate("text", x = 0, y = 200, label = unique(data$SA2_NAME16)) +
  # geom_point(aes(size = n_listings_int), alpha = 0.3) +
  geom_smooth(aes(group = interaction(SA2_NAME16, STE_NAME16),
                  col = STE_NAME16,
                  weight = n_listings_int),
              method = "lm", se = FALSE) +
  facet_wrap(STE_NAME16~SA2_NAME16) +
  theme_minimal() + xlab("") + ylab(("")) +
  theme(strip.text.x = element_blank()) 

# adr, smooth
ggplot(data, aes(x = time, y = adr_50p_int/1000, group = SA2_NAME16)) +
  # annotate("text", x = 0, y = 200, label = unique(data$SA2_NAME16)) +
  # geom_point(aes(size = n_listings_int), alpha = 0.3) +
  geom_smooth(aes(group = interaction(SA2_NAME16, STE_NAME16),
                  weight = n_listings_int, 
                  col = STE_NAME16), se = FALSE) +
  facet_wrap(STE_NAME16~SA2_NAME16) +
  theme_minimal() + xlab("") + ylab(("")) +
  theme(strip.text.x = element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(), 
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())

3 Models

3.1 random region, random state, no adr

m1 <- lme(rent_50p_int ~ time, 
          random = ~ 1 | STE_NAME16/SA2_MAIN16, 
          data = data)

# summary(m1)
# 
# data$m1_fit <- fitted(m1)
# 
# ggplot(data, aes(x = rent_50p_int)) +
#   geom_point(aes(y = m1_fit, size = n_listings_int), 
#              alpha = 0.3) +
#   geom_line(aes(y = rent_50p_int), col = "darkorchid4") +
#   facet_wrap(~STE_NAME16) +
#   theme_minimal() + xlab("Rent") + ylab("Predicted") 

3.2 random region, random state, with adr

m2 <- lme(rent_50p_int ~ adr_50p_int + time, 
          random = ~ 1 | STE_NAME16/SA2_MAIN16, 
          data = data)

# summary(m2)
# 
# # anova(m1, m2)
# 
# data$m2_fit <- fitted(m2)
# 
# ggplot(data, aes(x = rent_50p_int)) +
#   geom_point(aes(y = m2_fit, size = n_listings_int), 
#              alpha = 0.3) +
#   geom_line(aes(y = rent_50p_int), col = "darkorchid4") +
#   facet_wrap(~STE_NAME16) +
#   theme_minimal() + xlab("Rent") + ylab("Predicted") 
# 
# plot(ggpredict(m2, "time"))
# 
# plot(ggpredict(m2, terms = "adr_50p_int"))

3.3 random time region

m3 <- lme(rent_50p_int ~ adr_50p_int + time, 
          random = ~ time | STE_NAME16/SA2_MAIN16, 
          data = data)

# summary(m3)
# 
# # anova(m2, m3) 
# 
# data$m3_fit <- fitted(m3)
# 
# ggplot(data, aes(x = rent_50p_int)) +
#   geom_point(aes(y = m3_fit, size = n_listings_int), 
#              alpha = 0.3) +
#   geom_line(aes(y = rent_50p_int), col = "darkorchid4") +
#   facet_wrap(~STE_NAME16) +
#   theme_minimal() + xlab("Rent") + ylab("Predicted") 
# 
# plot(ggpredict(m3, "time"))
# # plot(ggpredict(m3, terms = c("time", "STE_NAME16")))
# 
# plot(ggpredict(m3, terms = "adr_50p_int"))
# # plot(ggpredict(m3, terms = c("adr_50p_int", "STE_NAME16")))

3.4 random region AR1

m4 <- lme(rent_50p_int ~ adr_50p_int + time, 
          random = ~ 1 | STE_NAME16/SA2_MAIN16, 
          correlation = corAR1(), 
          data = data)

# summary(m4)
# 
# # anova(m3, m4) 
# 
# data$m4_fit <- fitted(m4)
# 
# ggplot(data, aes(x = rent_50p_int)) +
#   geom_point(aes(y = m1_fit, size = n_listings_int), 
#              alpha = 0.3) +
#   geom_line(aes(y = rent_50p_int), col = "darkorchid4") +
#   facet_wrap(~STE_NAME16) +
#   theme_minimal() + xlab("Rent") + ylab("Predicted") 
# 
# plot(ggpredict(m4, "time"))
# 
# plot(ggpredict(m4, terms = "adr_50p_int"))

# fixef(m4)
# ranef(m4)
# plot(ACF(m4), alpha = 0.05)

3.5 random time region AR1

m5 <- lme(rent_50p_int ~ adr_50p_int + time, 
          random = ~ time | STE_NAME16/SA2_MAIN16, 
          correlation = corAR1(form = ~ time | STE_NAME16/SA2_MAIN16), 
          data = data)

# summary(m5)
# 
# # anova(m4, m5)
# # anova(m3, m5)
# # anova(m2, m5)
# 
# data$m5_fit <- fitted(m5)
# 
# ggplot(data, aes(x = rent_50p_int)) +
#   geom_point(aes(y = m1_fit, size = n_listings_int),
#              alpha = 0.3) +
#   geom_line(aes(y = rent_50p_int), col = "darkorchid4") +
#   facet_wrap(~STE_NAME16) +
#   theme_minimal() + xlab("Rent") + ylab("Predicted")
# 
# plot(ggpredict(m5, "time"))
# 
# plot(ggpredict(m5, terms = "adr_50p_int"))

# fixef(m5)
# ranef(m5)
# plot(ACF(m5), alpha = 0.05)

3.6 Comparison

compare_performance(m1, m2, m3, m4, m5)

## Random effect variances not available. Returned R2 does not account for random effects.
## Random effect variances not available. Returned R2 does not account for random effects.
## Random effect variances not available. Returned R2 does not account for random effects.

##   Model Type      AIC      BIC      RMSE R2_conditional R2_marginal       ICC
## 1    m1  lme 75327.56 75363.39 10.451215             NA  0.24964040        NA
## 2    m2  lme 75195.98 75238.99 10.369805             NA  0.38903552        NA
## 3    m3  lme 69065.01 69136.69  6.867411      0.9240143  0.05619619 0.9194899
## 4    m4  lme 63429.76 63479.94 11.738097             NA  0.09297640        NA
## 5    m5  lme 63221.49 63300.34  7.857693             NA          NA        NA
##                             BF
## 1                           NA
## 2 1032686528868859699840460824
## 3                          Inf
## 4                          Inf
## 5                          Inf

as_tibble(list(
  Model = as.character(c(1:5)),
  AIC = c(AIC(m1), AIC(m2), AIC(m3), AIC(m4), AIC(m5)))) %>% 
  ggplot() +
  geom_col(aes(y= AIC, x = Model)) +
  coord_flip() +
  theme_minimal()

3.7 Best model (AIC/BIC)

summary(m5)

## Linear mixed-effects model fit by REML
##  Data: data 
##        AIC      BIC    logLik
##   63221.49 63300.34 -31599.75
## 
## Random effects:
##  Formula: ~time | STE_NAME16
##  Structure: General positive-definite, Log-Cholesky parametrization
##             StdDev    Corr  
## (Intercept) 69.937911 (Intr)
## time         1.028778 -0.093
## 
##  Formula: ~time | SA2_MAIN16 %in% STE_NAME16
##  Structure: General positive-definite, Log-Cholesky parametrization
##             StdDev     Corr  
## (Intercept) 98.7837579 (Intr)
## time         0.5812841 -0.14 
## Residual     9.9861665       
## 
## Correlation Structure: AR(1)
##  Formula: ~time | STE_NAME16/SA2_MAIN16 
##  Parameter estimate(s):
##       Phi 
## 0.8247591 
## Fixed effects: rent_50p_int ~ adr_50p_int + time 
##                Value Std.Error   DF   t-value p-value
## (Intercept) 376.8260 26.771137 9228 14.075833  0.0000
## adr_50p_int   0.0017  0.002578 9228  0.663236  0.5072
## time          0.7417  0.373385 9228  1.986553  0.0470
##  Correlation: 
##             (Intr) ad_50_
## adr_50p_int -0.109       
## time        -0.097  0.013
## 
## Standardized Within-Group Residuals:
##          Min           Q1          Med           Q3          Max 
## -13.11558047  -0.41664183  -0.01358022   0.42731109  17.40627471 
## 
## Number of Observations: 9585
## Number of Groups: 
##                 STE_NAME16 SA2_MAIN16 %in% STE_NAME16 
##                          8                        355

# anova(m4, m5)
# anova(m3, m5)
# anova(m2, m5)

intervals(m5)

## Approximate 95% confidence intervals
## 
##  Fixed effects:
##                     lower          est.         upper
## (Intercept) 324.348699012 376.826045851 429.303392690
## adr_50p_int  -0.003343967   0.001709997   0.006763961
## time          0.009831793   0.741749658   1.473667522
## attr(,"label")
## [1] "Fixed effects:"
## 
##  Random Effects:
##   Level: STE_NAME16 
##                            lower        est.       upper
## sd((Intercept))       37.9739413 69.93791141 128.8070525
## sd(time)               0.9184956  1.02877821   1.1523023
## cor((Intercept),time) -0.3040087 -0.09335209   0.1260079
##   Level: SA2_MAIN16 
##                            lower       est.         upper
## sd((Intercept))       91.6417782 98.7837579 106.482338211
## sd(time)               0.4985769  0.5812841   0.677711365
## cor((Intercept),time) -0.2821126 -0.1400618   0.007998466
## 
##  Correlation structure:
##         lower      est.     upper
## Phi 0.7985849 0.8247591 0.8478197
## attr(,"label")
## [1] "Correlation structure:"
## 
##  Within-group standard error:
##     lower      est.     upper 
##  9.307009  9.986166 10.714884

plot_model(m5)

plot_model(m5, type = "slope")

plot_model(m5, type = "resid")

plot_model(m5, type = "diag")

## [[1]]

## 
## [[2]]

## 
## [[3]]

data$m5_fit <- fitted(m5)

ggplot(data, aes(x = rent_50p_int)) +
  geom_point(aes(y = m5_fit, 
                 size = record_count),
                 # size = n_listings_int),
             alpha = 0.3) +
  geom_line(aes(y = rent_50p_int), col = "darkorchid4") +
  facet_wrap(~STE_NAME16) +
  theme_minimal() + xlab("Rent") + ylab("Predicted")

# plot(ggpredict(m5, "time"))
# 
# plot(ggpredict(m5, terms = "adr_50p_int"))

p1 <- plot(ggpredict(m5, "time"))

p2 <- plot(ggpredict(m5, terms = "adr_50p_int"))

plot_grid(p1, p2)

3.8 Prediction examples

3.8.1 NSW

data %>% 
  filter(SA2_NAME16 %in% 
           c("Byron Bay", 
             "Avalon - Palm Beach")) %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = m5_fit, group = SA2_NAME16), 
            col = "darkorchid4") +
  geom_point(aes(y = rent_50p_int, size = record_count_int), 
             col = "#00AFBB", alpha = 0.3) +
  # geom_point(aes(y = adr_50p_int, size = n_listings_int), 
  #            col = "#E7B800", alpha = 0.33) +
  theme_minimal() + 
  xlab("") + ylab("Rent / fitted") + 
  facet_wrap(~SA2_NAME16, scales = "free_y")

3.8.2 Qld

data %>% 
  filter(SA2_NAME16 %in% 
           c("Coolangatta",
             "Scarborough - Newport - Moreton Island")) %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = m5_fit, group = SA2_NAME16), 
            col = "darkorchid4") +
  geom_point(aes(y = rent_50p_int, size = record_count_int), 
             col = "#00AFBB", alpha = 0.3) +
  # geom_point(aes(y = adr_50p_int, size = n_listings_int), 
  #            col = "#E7B800", alpha = 0.33) +
  theme_minimal() + 
  xlab("") + ylab("Rent / fitted") + 
  facet_wrap(~SA2_NAME16, scales = "free_y")

3.9 ST outliers

data$diff <- (data$rent_50p_int - data$m5_fit) / data$rent_50p_int

3.9.1 Means from the whole time period

data %>% 
  group_by(SA2_NAME16) %>% 
  summarise(mean_diff = mean(diff),
            STE_NAME16 = first(STE_NAME16),
            n_listings_int = min(n_listings_int)) %>% 
  arrange(mean_diff) %>% 
  print(n = 50)

## # A tibble: 355 x 4
##    SA2_NAME16                           mean_diff STE_NAME16      n_listings_int
##    <chr>                                    <dbl> <chr>                    <int>
##  1 Gladstone                             -0.0188  Queensland                  10
##  2 Portland                              -0.0174  Victoria                    14
##  3 Eden                                  -0.0170  New South Wales             14
##  4 Brunswick Heads - Ocean Shores        -0.0167  New South Wales             67
##  5 Peregian Beach - Marcus Beach         -0.0161  Queensland                  26
##  6 South Townsville - Railway Estate     -0.0158  Queensland                  12
##  7 Maylands                              -0.0156  Western Austra~             40
##  8 Rockingham                            -0.0142  Western Austra~             13
##  9 Mansfield (Vic.)                      -0.0132  Victoria                    40
## 10 Avoca Beach - Copacabana              -0.0132  New South Wales             66
## 11 Como                                  -0.0124  Western Austra~             14
## 12 Halls Head - Erskine                  -0.0121  Western Austra~             10
## 13 Eimeo - Rural View                    -0.0118  Queensland                  20
## 14 Lenah Valley - Mount Stuart           -0.0115  Tasmania                    20
## 15 Applecross - Ardross                  -0.0114  Western Austra~             19
## 16 Mandurah                              -0.0108  Western Austra~             60
## 17 Tully                                 -0.0108  Queensland                  18
## 18 East Victoria Park - Carlisle         -0.0107  Western Austra~             18
## 19 Mosman Park - Peppermint Grove        -0.0104  Western Austra~             37
## 20 Woolgoolga - Arrawarra                -0.0101  New South Wales             12
## 21 Orange                                -0.0100  New South Wales             16
## 22 Coolangatta                           -0.0100  Queensland                  75
## 23 Scarborough                           -0.00980 Western Austra~             80
## 24 Maclean - Yamba - Iluka               -0.00978 New South Wales             42
## 25 Agnes Water - Miriam Vale             -0.00953 Queensland                  21
## 26 Victoria Park - Lathlain - Burswood   -0.00915 Western Austra~             46
## 27 Wembley - West Leederville - Glenda~  -0.00896 Western Austra~             52
## 28 South Perth - Kensington              -0.00864 Western Austra~            107
## 29 Kingston Beach - Blackmans Bay        -0.00825 Tasmania                    11
## 30 Joondalup - Edgewater                 -0.00815 Western Austra~             17
## 31 Fremantle - South                     -0.00815 Western Austra~             47
## 32 South Brisbane                        -0.00805 Queensland                  92
## 33 Avalon - Palm Beach                   -0.00803 New South Wales             91
## 34 Belmont - Ascot - Redcliffe           -0.00794 Western Austra~             13
## 35 Kelvin Grove - Herston                -0.00780 Queensland                  31
## 36 Albury - South                        -0.00749 New South Wales             21
## 37 Warrnambool - South                   -0.00749 Victoria                    31
## 38 Claremont (WA)                        -0.00735 Western Austra~             33
## 39 Broome                                -0.00727 Western Austra~             39
## 40 Jindabyne - Berridale                 -0.00721 New South Wales            134
## 41 South Hobart - Fern Tree              -0.00697 Tasmania                    30
## 42 Magnetic Island                       -0.00676 Queensland                  51
## 43 Townsville City - North Ward          -0.00663 Queensland                  50
## 44 Sunshine Beach                        -0.00632 Queensland                 106
## 45 Mount Martha                          -0.00629 Victoria                    22
## 46 Fortitude Valley                      -0.00626 Queensland                 148
## 47 Nedlands - Dalkeith - Crawley         -0.00616 Western Austra~             38
## 48 Mount Hawthorn - Leederville          -0.00598 Western Austra~             16
## 49 Fitzroy                               -0.00585 Victoria                   197
## 50 East Brisbane                         -0.00576 Queensland                  15
## # ... with 305 more rows

data %>% 
  filter(SA2_NAME16 %in% 
           c("Gladstone", 
             "Peregian Beach - Marcus Beach")) %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = m5_fit, group = SA2_NAME16), 
            col = "darkorchid4") +
  geom_point(aes(y = rent_50p_int, size = record_count_int), 
             col = "#00AFBB", alpha = 0.3) +
  geom_point(aes(y = adr_50p_int, size = n_listings_int),
             col = "#E7B800", alpha = 0.33) +
  theme_minimal() + 
  xlab("") + ylab("Rent / fitted") + 
  facet_wrap(~SA2_NAME16, scales = "free_y")

data %>% 
  group_by(SA2_NAME16) %>% 
  summarise(mean_diff = mean(diff),
            STE_NAME16 = first(STE_NAME16),
            n_listings_int = min(n_listings_int)) %>% 
  arrange(desc(mean_diff)) %>% 
  print(n = 50)

## # A tibble: 355 x 4
##    SA2_NAME16                            mean_diff STE_NAME16     n_listings_int
##    <chr>                                     <dbl> <chr>                   <int>
##  1 Kiama                                   0.0114  New South Wal~             36
##  2 Cremorne - Cammeray                     0.00961 New South Wal~            195
##  3 Torquay                                 0.00909 Victoria                   81
##  4 West Hobart                             0.00854 Tasmania                   35
##  5 Old Bar - Manning Point - Red Head      0.00820 New South Wal~             15
##  6 Willoughby - Castle Cove - Northbrid~   0.00812 New South Wal~             27
##  7 Broadbeach Waters                       0.00775 Queensland                 24
##  8 Macquarie Park - Marsfield              0.00745 New South Wal~             17
##  9 Botany                                  0.00739 New South Wal~             15
## 10 Main Beach                              0.00718 Queensland                 34
## 11 Hope Island                             0.00688 Queensland                 25
## 12 North Sydney - Lavender Bay             0.00682 New South Wal~            237
## 13 Southport - North                       0.00674 Queensland                 37
## 14 Sans Souci - Ramsgate                   0.00666 New South Wal~             11
## 15 Dover Heights                           0.00650 New South Wal~            144
## 16 South Yarra - East                      0.00641 Victoria                  559
## 17 Surry Hills                             0.00618 New South Wal~            612
## 18 Darlinghurst                            0.00617 New South Wal~            671
## 19 Kensington (NSW)                        0.00608 New South Wal~             72
## 20 Port Douglas                            0.00601 Queensland                241
## 21 Brighton (Vic.)                         0.00598 Victoria                   33
## 22 Coogee - Clovelly                       0.00595 New South Wal~            654
## 23 Gladesville - Huntleys Point            0.00593 New South Wal~             20
## 24 Forster                                 0.00574 New South Wal~             26
## 25 Ryde                                    0.00564 New South Wal~             18
## 26 Collingwood                             0.00560 Victoria                  133
## 27 Bondi Junction - Waverly                0.00557 New South Wal~            352
## 28 South Yarra - West                      0.00551 Victoria                  115
## 29 Bondi Beach - North Bondi               0.00542 New South Wal~           1782
## 30 Sydney - Haymarket - The Rocks          0.00541 New South Wal~            761
## 31 Chatswood (West) - Lane Cove North      0.00532 New South Wal~             57
## 32 Concord West - North Strathfield        0.00531 New South Wal~             73
## 33 Haberfield - Summer Hill                0.00525 New South Wal~             19
## 34 Randwick - South                        0.00521 New South Wal~            182
## 35 Buderim - North                         0.00519 Queensland                 10
## 36 Palm Beach                              0.00509 Queensland                 61
## 37 Southport - South                       0.00507 Queensland                 26
## 38 Currumbin - Tugun                       0.00490 Queensland                 67
## 39 Mascot - Eastlakes                      0.00481 New South Wal~             62
## 40 Rosebud - McCrae                        0.00478 Victoria                   44
## 41 Fitzroy North                           0.00472 Victoria                   88
## 42 Glebe - Forest Lodge                    0.00467 New South Wal~            271
## 43 Kingsford                               0.00461 New South Wal~             67
## 44 Box Hill                                0.00461 Victoria                   25
## 45 Marcoola - Mudjimba                     0.00457 Queensland                 26
## 46 Woollahra                               0.00443 New South Wal~            118
## 47 Double Bay - Bellevue Hill              0.00442 New South Wal~            536
## 48 Neutral Bay - Kirribilli                0.00441 New South Wal~            387
## 49 Chatswood (East) - Artarmon             0.00440 New South Wal~             52
## 50 Ashfield                                0.00431 New South Wal~             38
## # ... with 305 more rows

data %>% 
  filter(SA2_NAME16 %in% 
           c("Broadbeach Waters", 
             "Torquay")) %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = m5_fit, group = SA2_NAME16), 
            col = "darkorchid4") +
  geom_point(aes(y = rent_50p_int, size = record_count_int), 
             col = "#00AFBB", alpha = 0.3) +
  geom_point(aes(y = adr_50p_int, size = n_listings_int),
             col = "#E7B800", alpha = 0.33) +
  theme_minimal() + 
  xlab("") + ylab("Rent / fitted") + 
  facet_wrap(~SA2_NAME16, scales = "free_y")

3.9.2 Means from the individual times

data %>% 
  select(SA2_NAME16, STE_NAME16, date, n_listings_int, diff) %>% 
  arrange(diff) %>% 
  print(n = 50)

## # A tibble: 9,585 x 5
##    SA2_NAME16                   STE_NAME16     date       n_listings_int    diff
##    <chr>                        <chr>          <date>              <int>   <dbl>
##  1 Avoca Beach - Copacabana     New South Wal~ 2018-07-01            133 -0.437 
##  2 Avoca Beach - Copacabana     New South Wal~ 2018-06-01            130 -0.436 
##  3 Broadbeach Waters            Queensland     2017-05-01             30 -0.102 
##  4 Brunswick Heads - Ocean Sho~ New South Wal~ 2018-02-01             83 -0.101 
##  5 Jindabyne - Berridale        New South Wal~ 2018-02-01            301 -0.0994
##  6 Brunswick Heads - Ocean Sho~ New South Wal~ 2018-01-01             82 -0.0967
##  7 Jindabyne - Berridale        New South Wal~ 2018-09-01            416 -0.0938
##  8 Brunswick Heads - Ocean Sho~ New South Wal~ 2017-12-01             86 -0.0923
##  9 Narooma - Bermagui           New South Wal~ 2017-02-01             23 -0.0895
## 10 Jindabyne - Berridale        New South Wal~ 2018-01-01            277 -0.0841
## 11 Brunswick Heads - Ocean Sho~ New South Wal~ 2017-10-01             86 -0.0831
## 12 Broadbeach Waters            Queensland     2017-01-01             26 -0.0806
## 13 Narooma - Bermagui           New South Wal~ 2017-01-01             23 -0.0802
## 14 Eimeo - Rural View           Queensland     2017-12-01             29 -0.0796
## 15 Narooma - Bermagui           New South Wal~ 2017-03-01             24 -0.0744
## 16 Albury - South               New South Wal~ 2018-12-01             34 -0.0744
## 17 Brunswick Heads - Ocean Sho~ New South Wal~ 2017-08-01             86 -0.0739
## 18 Portland                     Victoria       2018-09-01             20 -0.0735
## 19 Eimeo - Rural View           Queensland     2017-11-01             28 -0.0734
## 20 Carlton                      Victoria       2017-10-01            271 -0.0734
## 21 Main Beach                   Queensland     2017-02-01             38 -0.0725
## 22 Fremantle - South            Western Austr~ 2017-11-01             53 -0.0722
## 23 Narooma - Bermagui           New South Wal~ 2016-12-01             21 -0.0703
## 24 Peregian Beach - Marcus Bea~ Queensland     2018-04-01             60 -0.0702
## 25 Scarborough - Newport - Mor~ Queensland     2018-08-01             23 -0.0695
## 26 Brunswick Heads - Ocean Sho~ New South Wal~ 2017-07-01             82 -0.0694
## 27 Peregian Beach - Marcus Bea~ Queensland     2017-12-01             43 -0.0688
## 28 Carlton                      Victoria       2017-09-01            249 -0.0680
## 29 Broadbeach Waters            Queensland     2017-06-01             30 -0.0677
## 30 Portland                     Victoria       2018-08-01             20 -0.0676
## 31 Eimeo - Rural View           Queensland     2017-10-01             28 -0.0672
## 32 Torquay                      Victoria       2019-01-01            103 -0.0671
## 33 Kiama                        New South Wal~ 2016-12-01             36 -0.0663
## 34 Main Beach                   Queensland     2018-11-01            119 -0.0655
## 35 Williamstown                 Victoria       2017-04-01             18 -0.0655
## 36 Broadbeach Waters            Queensland     2017-08-01             33 -0.0654
## 37 Mount Eliza                  Victoria       2017-09-01             13 -0.0652
## 38 Brunswick Heads - Ocean Sho~ New South Wal~ 2017-06-01             78 -0.0650
## 39 Lenah Valley - Mount Stuart  Tasmania       2018-05-01             20 -0.0649
## 40 Mount Hawthorn - Leederville Western Austr~ 2017-10-01             27 -0.0646
## 41 Scarborough                  Western Austr~ 2018-01-01            124 -0.0644
## 42 Botany                       New South Wal~ 2019-01-01             25 -0.0643
## 43 Old Bar - Manning Point - R~ New South Wal~ 2017-03-01             20 -0.0638
## 44 Mansfield (Vic.)             Victoria       2018-02-01             62 -0.0638
## 45 Lenah Valley - Mount Stuart  Tasmania       2018-04-01             21 -0.0637
## 46 Fremantle - South            Western Austr~ 2017-09-01             53 -0.0633
## 47 Cottesloe                    Western Austr~ 2017-08-01             97 -0.0626
## 48 Airlie - Whitsundays         Queensland     2017-04-01            332 -0.0625
## 49 Maylands                     Western Austr~ 2017-10-01             41 -0.0622
## 50 Portland                     Victoria       2018-07-01             20 -0.0619
## # ... with 9,535 more rows

data %>% 
  filter(SA2_NAME16 %in% 
           c("Avoca Beach - Copacabana", 
             "Brunswick Heads - Ocean Shores")) %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = m5_fit, group = SA2_NAME16), 
            col = "darkorchid4") +
  geom_point(aes(y = rent_50p_int, size = record_count_int), 
             col = "#00AFBB", alpha = 0.3) +
  geom_point(aes(y = adr_50p_int, size = n_listings_int),
             col = "#E7B800", alpha = 0.33) +
  theme_minimal() + 
  xlab("") + ylab("Rent / fitted") + 
  facet_wrap(~SA2_NAME16, scales = "free_y")

data %>% 
  select(SA2_NAME16, STE_NAME16, date, n_listings_int, diff) %>% 
  arrange(desc(diff)) %>% 
  print(n = 30)

## # A tibble: 9,585 x 5
##    SA2_NAME16                   STE_NAME16      date       n_listings_int   diff
##    <chr>                        <chr>           <date>              <int>  <dbl>
##  1 Narooma - Bermagui           New South Wales 2018-08-01             55 0.386 
##  2 Fremantle                    Western Austra~ 2019-01-01            266 0.0850
##  3 Broadbeach Waters            Queensland      2018-01-01             44 0.0832
##  4 Main Beach                   Queensland      2017-10-01             67 0.0822
##  5 Main Beach                   Queensland      2017-11-01             70 0.0821
##  6 Main Beach                   Queensland      2018-01-01             77 0.0816
##  7 Main Beach                   Queensland      2018-03-01            101 0.0812
##  8 Caloundra - Kings Beach      Queensland      2019-01-01            120 0.0790
##  9 Broadbeach Waters            Queensland      2018-02-01             48 0.0788
## 10 Broadbeach Waters            Queensland      2018-03-01             62 0.0744
## 11 Main Beach                   Queensland      2017-12-01             76 0.0734
## 12 Broadbeach Waters            Queensland      2018-04-01             66 0.0700
## 13 Kiama                        New South Wales 2017-09-01             67 0.0699
## 14 Kiama                        New South Wales 2017-06-01             62 0.0674
## 15 Old Bar - Manning Point - R~ New South Wales 2017-07-01             20 0.0669
## 16 Phillip Island               Victoria        2018-06-01            126 0.0657
## 17 Broadbeach Waters            Queensland      2018-05-01             68 0.0656
## 18 Main Beach                   Queensland      2018-02-01             80 0.0644
## 19 Main Beach                   Queensland      2018-04-01            111 0.0638
## 20 Airlie - Whitsundays         Queensland      2018-04-01            431 0.0637
## 21 West Hobart                  Tasmania        2017-03-01             43 0.0634
## 22 Old Bar - Manning Point - R~ New South Wales 2017-08-01             20 0.0629
## 23 Broadbeach Waters            Queensland      2018-06-01             70 0.0613
## 24 Phillip Island               Victoria        2018-07-01            128 0.0600
## 25 Kiama                        New South Wales 2017-05-01             57 0.0598
## 26 Fremantle - South            Western Austra~ 2019-01-01             65 0.0594
## 27 Old Bar - Manning Point - R~ New South Wales 2017-09-01             20 0.0588
## 28 Airlie - Whitsundays         Queensland      2018-05-01            433 0.0587
## 29 West Hobart                  Tasmania        2017-04-01             43 0.0586
## 30 Narooma - Bermagui           New South Wales 2018-11-01             72 0.0586
## # ... with 9,555 more rows

data %>% 
  filter(SA2_NAME16 %in% 
           c("Fremantle", 
             "Main Beach")) %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = m5_fit, group = SA2_NAME16), 
            col = "darkorchid4") +
  geom_point(aes(y = rent_50p_int, size = record_count_int), 
             col = "#00AFBB", alpha = 0.3) +
  geom_point(aes(y = adr_50p_int, size = n_listings_int),
             col = "#E7B800", alpha = 0.33) +
  theme_minimal() + 
  xlab("") + ylab("Rent / fitted") + 
  facet_wrap(~SA2_NAME16, scales = "free_y")